Results 21 to 30 of about 142,624 (305)
A multi-dimensional semantic pseudo-relevance feedback framework for information retrieval. [PDF]
Sci RepPre-trained models have garnered significant attention in the field of information retrieval, particularly for improving document ranking. Typically, an initial retrieval step using sparse methods such as BM25 is employed to obtain a set of pseudo ...
Pan M +4 more
europepmc +2 more sources
Visual Re-Ranking for Multi-Aspect Information Retrieval [PDF]
Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, 2017 We present visual re-ranking, an interactive visualization technique for multi-aspect information retrieval. In multi-aspect search, the information need of the user consists of more than one aspect or query simultaneously. While visualization and interactive search user interface techniques for improving user interpretation of search results have been
Khalil Klouche +4 more
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A Deep Look into neural ranking models for information retrieval [PDF]
Information Processing & Management, 2019 Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods.
Jiafeng Guo +8 more
openalex +4 more sources
Configurable indexing and ranking for XML information retrieval [PDF]
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, 2004 Indexing and ranking are two key factors for efficient and effective XML information retrieval. Inappropriate indexing may result in false negatives and false positives, and improper ranking may lead to low precisions. In this paper, we propose a configurable XML information retrieval system, in which users can configure appropriate index types for XML
Shaorong Liu, Qinghua Zou, Wesley W. Chu
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Generalized ensemble model for document ranking in information retrieval
Computer Science and Information Systems, 2016 A generalized ensemble model (gEnM) for document ranking is proposed in this paper. The gEnM linearly combines the document retrieval models and tries to retrieve relevant documents at high positions. In order to obtain the optimal linear combination of multiple document retrieval models or rankers, an optimization program is formulated by ...
Yanshan Wang, In-Chan Choi, Hongfang Liu
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On the limitations of document ranking algorithms in information retrieval [PDF]
Proceedings of the 4th annual international ACM SIGIR conference on Information storage and retrieval theoretical issues in information retrieval - SIGIR '81, 1981 A document retrieval system should rank documents in order of their usefulness or satisfaction to the users. This principle was first explicated in the classic paper by Maron and Kuhns (1). Additional considerations concerning document ranking have been suggested by other researchers (2,3).
Keith H. Stirling
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Bandit algorithms in information retrieval evaluation and ranking
Journal of Physics: Conference Series, 2019 Abstract Bandit algorithms have been widely used in many application areas including information retrieval evaluation and ranking. This is largely due to their exceptional performance. The aim of this study is to examine the overall published studies in terms of trends that shape the use of bandit algorithms in the evaluation and ranking
Sinyinda Muwanei +3 more
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Distributed ranking methods for geographic information retrieval [PDF]
, 2004 Geographic Information Retrieval is concerned with retrieving documents that are related to some location. This paper addresses the ranking of documents by both textual relevance and spatial relevance. To this end, we introduce distributed ranking, where
Arampatzis, Avi +2 more
core +2 more sources
Cross-Modal Retrieval by Class Information and Listwise Ranking
Jisuanji kexue yu tansuo, 2021 Cross-modal retrieval has attracted significant attention due to the increasing use of multi-modal data. A major challenge for cross-modal retrieval is the modal gap. To cope with the heterogeneity, common subspace learning method is proposed.
LIU Yuping, GE Hong, ZENG Yibin
doaj +1 more source
Document Retrieval for Precision Medicine Using a Deep Learning Ensemble Method
JMIR Medical Informatics, 2021 BackgroundWith the development of biomedicine, the number of biomedical documents has increased rapidly bringing a great challenge for researchers trying to retrieve the information they need.
Zhiqiang Liu +3 more
doaj +1 more source